Adaptive Realized Kernels
type: Dataset
description: We design adaptive realized kernels to estimate the integrated volatility in a framework that combines a stochastic volatility model with leverage effect for the efficient price and a semiparametric microstructure noise model specied at the highest frequency. Some time dependence parameters of the noise model must be estimated before adaptive realized kernels can be implemented. We study their performance by simulation and illustrate their use with twelve stocks listed in the Dow Jones Industrial. As expected, we nd that adaptive realized kernels achieves the optimal trade-off between the discretization error and the microstructure noise.
landing page: https://hal.science/hal-00867967
keyword: infoeu-reposemanticspreprint
keyword: integrated-volatility
keyword: method-of-moment
keyword: microstructure-noise
keyword: preprints-working-papers-
keyword: realized-kernels
keyword: shsecohumanities-and-social-scienceseconomics-and-finance
issued: 2026-05-09T13:16:27.572714
modified: 2026-05-09T13:16:27.572718
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Adaptive Realized Kernels
type: Distribution
format: HTML
access URL: https://hal.science/hal-00867967
issued: 2026-05-09T13:16:27.578170
modified: 2026-05-09T13:16:27.563876